Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods

In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness

Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods

In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness

Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy

Text-to-image synthesis has recently attracted widespread attention due to rapidly improving quality and numerous practical applications. However, the language understanding capabilities of text-to-image models are still poorly understood, which makes it difficult to reason about prompt formulations that a given model would understand well. In this work, we measure the capability of popular text-to-image models to understand $\textit{hypernymy}$, or the "is-a" relation between words. We design two automatic metrics based on the WordNet semantic hierarchy and existing image classifiers pretrained on ImageNet. These metrics both enable broad quantitative comparison of linguistic capabilities for text-to-image models and offer a way of finding fine-grained qualitative differences, such as words that are unknown to models and thus are difficult for them to draw. We comprehensively evaluate popular text-to-image models, including GLIDE, Latent Diffusion, and Stable Diffusion, showing how our metrics can provide a better understanding of the individual strengths and weaknesses of these models

4D City transformations by time series of aerial images

Recent photogrammetric applications, based on dense image matching algorithms, allow to use not only images acquired by digital cameras, amateur or not, but also to recover the vast heritage of analogue photographs. This possibility opens up many possibilities in the use and enhancement of existing photos heritage. The research of the original figuration of old buildings, the virtual reconstruction of disappeared architectures and the study of urban development are some of the application areas that exploit the great cultural heritage of photography. Nevertheless there are some restrictions in the use of historical images for automatic reconstruction of buildings such as image quality, availability of camera parameters and ineffective geometry of image acquisition. These constrains are very hard to solve and it is difficult to discover good dataset in the case of terrestrial close range photogrammetry for the above reasons. Even the photographic archives of museums and superintendence, while retaining a wealth of documentation, have no dataset for a dense image matching approach. Compared to the vast collection of historical photos, the class of aerial photos meets both criteria stated above. In this paper historical aerial photographs are used with dense image matching algorithms to realize 3d models of a city in different years. The models can be used to study the urban development of the city and its changes through time. The application relates to the city centre of Verona, for which some time series of aerial photographs have been retrieved. The models obtained in this way allowed, right away, to observe the urban development of the city, the places of expansion and new urban areas. But a more interesting aspect emerged from the analytical comparison between models. The difference, as the Euclidean distance, between two models gives information about new buildings or demolitions. As considering accuracy it is necessary point out that the quality of final observations from model comparison depends on several aspects such as image quality, image scale and marker accuracy from cartography

An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies

The development of two-dimensional (2D) bulge-to-disk decomposition techniques has shown their advantages over traditional one-dimensional (1D) techniques, especially for galaxies with non-axisymmetric features. However, the full potential of 2D techniques has yet to be fully exploited. Secondary morphological features in nearby disk galaxies, such as bars, lenses, rings, disk breaks, and spiral arms, are seldom accounted for in 2D image decompositions, even though some image-fitting codes, such as GALFIT, are capable of handling them. We present detailed, 2D multi-model and multi-component decomposition of high-quality $R$-band images of a representative sample of nearby disk galaxies selected from the Carnegie-Irvine Galaxy Survey, using the latest version of GALFIT. The sample consists of five barred and five unbarred galaxies, spanning Hubble types from S0 to Sc. Traditional 1D decomposition is also presented for comparison. In detailed case studies of the 10 galaxies, we successfully model the secondary morphological features. Through a comparison of best-fit parameters obtained from different input surface brightness models, we identify morphological features that significantly impact bulge measurements. We show that nuclear and inner lenses/rings and disk breaks must be properly taken into account to obtain accurate bulge parameters, whereas outer lenses/rings and spiral arms have a negligible effect. We provide an optimal strategy to measure bulge parameters of typical disk galaxies, as well as prescriptions to estimate realistic uncertainties of them, which will benefit subsequent decomposition of a larger galaxy sample.Comment: 30 pages, 14 figures, published in ApJ; minor typos correcte

DeepIron: Predicting Unwarped Garment Texture from a Single Image

Realistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single image with pose. Assuming that 3D garments are modeled by stitching 2D garment sewing patterns, our specific goal is to generate a texture image for the sewing patterns. A key component of our framework, the Texture Unwarper, infers the original texture image from the input clothing image, which exhibits warping and occlusion of texture due to the user's body shape and pose. The Texture Unwarper effectively transforms between the input and output images by mapping the latent spaces of the two images. By inferring the unwarped original texture of the input garment, our method helps reconstruct 3D garment models that can show high-quality texture images realistically deformed for new poses. We validate the effectiveness of our approach through a comparison with other methods and ablation studies